Register today for exclusive training designed for attendees
Delivered virtually by a live expert, these training classes were created specially for full conference attendees of OPTIMIZE 2021. These exclusive classes will give you hands-on experience using the latest innovations to solve real-world business problems. Classes will be delivered on May 21, 2021.
Early bird registrants can take these exclusive classes for the heavily discounted rate of just $125 (USD). To receive discounted training classes, please complete your registration for OPTIMIZE 2021; you will then receive an email containing a discount code for training.
Register early for the best pricing:
Early bird price: before February 15 - $99 (USD)
Mid term price: February 15 - April 5 - $125 (USD)
S&OP is a monthly decision process that spans demand planning, supply planning, pre-S & OP, and executive S&OP steps. S&OE is a daily and weekly process related to executing the monthly S&OP plan and best practices to close the gaps between plans and actuals. During this course, we key ideas such as data governance, management of crucial meetings, tracking of S&OP action items, and knowledge management and discuss scheduling best practices.
As CO2 capture through chemical and physical solvents grows as a business necessity to drive towards meeting environmental regulations and increasing energy efficiency, Aspen Rate-Based Distillation technology offers a tool to create reliable models to aid in the decision-making at the design or operation stages of these type of processes.
Aspen Multi-Case streamlines the process simulation process by parallelizing simulation runs on multiple cores for multiple Aspen Plus® and Aspen HYSYS® cases. This course we will showcase the power of Aspen Multi-Case and how it can accelerate time of process simulation results.
Current estimating practices are focused on single users and their own individual database. If they want to show results to other parties, they would need to generate new document-based reports each time that the estimator receives new feedback. Aspen Capital Cost Estimator Insights ™ extends the value of Aspen Enterprise Insights by allowing users to improve estimating practices in a company, by creating a central hub for real time updates for the project to all the relevant parties.
Developing a 3D conceptual layout is critical during the pre-FEED and FEED stages of a project to optimize and validate the design and to support a cost estimate. OptiPlant 3D is the leading automated conceptual design tool to develop a multi-discipline layout. Leveraging existing data from process simulation or a cost project, this excel data can be imported into OptiPlant to automate the 3D parametric modeling of equipment and structures. From there automatic pipe routing, electrical routing, and foundation calculations can complete the 3D layout and generates bulk material quantities.
To provide systematic training in Aspen Unified PIMS (AUP) for refinery Linear Programming (LP) planners. Detailed emphasis is placed on introducing and reviewing advanced features and functions in Aspen Unified PIMS, including Planning Work Area, Price Catalog, Flowsheet Model Building, Model Life Cycle Management, etc.
Introduce scheduling workflows in Aspen Unified Scheduling (AUS) for refinery schedulers. The course covers the basic Unified Scheduling capabilities, including Scheduling Work Area, Gantt Chart Functionalities, Scheduling Logics, Case Management and Schedule Optimization.
Increase Production System Reliability and Understand the costs and benefits of making such changes drives the decision-making process. Use Fidelis features such as turn-ups, warehouse sparing, cold standby redundancy, usage of Fidelis Utility and advanced features related to submodels and custom key routines. Introduce Fidelis synergies with two other APM products: Aspen Mtell® and Aspen Event Analytics ™. Aspen Mtell can predict future mechanical failures for which Fidelis can quantify their impact on your system performance. Aspen Event Analytics can discover patterns in your sensors data to identify events that you can then include in Fidelis models.
Build a batch and continuous process agent and then deploy the agent online and will discuss how Mtell and ProMV can complement each other's ability to alert for process anomalies and prompt operations to respond most effectively. The last session covers how Event Analytics ability to troubleshoot alerts when history is lacking complements ProMV's alerts based on history.
Process Safety is of vital importance as it helps prevent major disasters involving the consequences of releases of toxic, reactive, flammable or explosive chemicals. Pressurized equipment in a chemical plant must be protected against overpressure scenarios (e.g., fire emergencies, control valve failure, tube rupture of shell-and-tube heat exchangers, etc.) with pressure relief devices (PRDs). Furthermore, documentation needs to be prepared for all pressure safety valves (PSVs), rupture disks and storage tank vent valves, as it plays an important role in Process Safety Management.
Process industries are rapidly implementing AI and Machine Learning to capture the benefits while overcoming the challenges of traditional models. AspenTech Hybrid Models ™ is enabling the process industries to reduce operating costs, understand difficult processes, perform production transition to new products and improve product quality.
First principle process model has been used to support plant operation in the process industry. The model must be calibrated with plant data before it can be used to reliably and accurately predict the plant performance. Plant Data feature in Aspen Plus and Aspen HYSYS enables simulation user to import plant data from either Excel or data historian, condition raw data, run model with conditioned plant data, and then deploy the model online. In this course you will build a Plant Digital Twin solution for Emissions Monitoring.
Organizations can improve operating margins across the enterprise with greater visibility into APC benefits and performance with Centralized Performance Monitoring. The new Calibrate engine (Calibrate 2.0) simplifies the model update process with more intuitive configuration to maintain Aspen DMC3 controller benefits. The new non-linear capabilities of Aspen DMC3 enables extending Aspen DMC3 controller features and functionality to non-linear processes. Organizations can now use one platform and technology for both linear and non-linear processes, drastically reducing the learning curve and simplifying maintenance. Apply these new Aspen DMC3 features to improve APC benefits and simplify deployments.
With the release of aspenONE® V12, an AI-based framework is provided for the development, online deployment and monitoring of Deep Learning models for Aspen IQs. Deep Learning TensorFlow algorithms are embedded in this framework: GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory) and MLP (Multilaye perceptron). Deep Learning nonlinear models provide accurate inference of process and quality variables across a wide range of operating conditions. Aspen Watch Performance Monitor™ is fully supported to leverage reports and dashboards and monitor KPIs in real time for inferentials including Deep Learning models.
Advanced Process Control has been a reality in the continuous process industry for over 25 years. However, the application of advanced control technologies to the Batch industry has been scattered and mostly concentrated on the batch monitoring. There are many challenges involved in controlling and optimizing a batch process such as nonlinear time-varying, quality variables available only after completion of batch and manipulated variables have very different effects at different times during the batch. In this context, there is an opportunity to better control product qualities at specified targets and optimize batch cycle times by implementing an advanced control strategy that can predict final product quality and optimize decision variables. This course is an introduction to Aspen Batch APC technology. You will learn how to model and control the final product quality of batch products to reduce product quality variation, increase productivity per batch and reduce batch cycle times.
Dynamic Optimization is a new area of economic opportunity in Production Optimization. GDOT ™ improves overall site operating margins by closing the loop between planning / economics objectives and actual operations of units through real time coordination of several APC applications across a broad envelope. In this class, learn how to define applications and estimate benefits, align with planning and scheduling work processes, online monitoring capabilities, and best practices for sustaining benefits. Class includes hands-on exercises on deploying GDOT templates.
The Aspen MES Collaborative is a collection of IP.21 servers that act in concert to scale and manage data loading. It’s a highly responsive high-availability solution with transparent, non-disruptive maintenance and upgrading. It’s also an enterprise-level historian allowing connectivity to small sites that cannot justify expenditure for a dedicated system at each site. The MES Collaborative provides true high availability eliminating the ripple effect of data loss. In addition, it can be combined with load balancing enabling the system to be highly responsive during high traffic.
Use automated Machine Learning to stop machines from breaking down, make them last longer, reduce maintenance costs, and increase the net product output of any process. In this course you will implement advanced agent building techniques, select appropriate agent training parameters, and troubleshoot agents for maximum performance.